Using the MyOpenMath generated hockey data, create the following charts:
1) A bar chart of the divisions showing how many teams are in each one.
2) A histogram of goals allowed. Describe the shape of this data (symmetric, normal, skewed (if so, with the correct direction)).
3) Box plots of goals scored grouped by division. Make a conclusion about the variance of goals scored based on these box plots. (i.e. which divisions have larger/smaller variance, which ones are similar, etc)
4) A Q-Q plot for goal differential. Then make a conclusion (using just the plot, don’t stress about different statistics) on whether this variable follows a normal distribution. (Note that this part is NOT included in the example provided. A lot of coding involves researching how to do things on your own, so I’m testing your research skills here a bit!)
You might not know what all of the variables mean in this data set. That’s ok, and part of data science — learning about things you aren’t familiar with. (I worked on a clinical team where I had to learn about bilirubins, something I’d literally never heard of until thrust into that role!) On this hockey data, I can assure you that a quick google search will answer any questions you have about what the different things mean.
The using the MyOpenMath generated house data (note that this will be a *new* data set from the previous assignment), create the following charts:
5) A bar chart showing the number of stories of the houses.
6) A histogram of bedrooms. Describe the shape of this data (symmetric, normal, skewed (if so, with the correct direction)).
7) Box plots of square footage grouped by the number of stories.
8) A Q-Q plot for square footage. Then make a conclusion (using just the plot, don’t stress about different statistics) on whether this variable follows a normal distribution.